pairwise_comparisons_between.md
In pairwiseComparisons: Multiple Pairwise Comparison Tests

`pairwise_comparisons()` works for between-subjects design

Code
  list(df1, df2, df3, df4, df5)
Output
  [[1]]
  # A tibble: 6 x 6
    group1  group2  p.value test.details     p.value.adjustment
    <chr>   <chr>     <dbl> <chr>            <chr>             
  1 carni   herbi     1     Student's t-test Bonferroni        
  2 carni   insecti   1     Student's t-test Bonferroni        
  3 carni   omni      1     Student's t-test Bonferroni        
  4 herbi   insecti   1     Student's t-test Bonferroni        
  5 herbi   omni      0.979 Student's t-test Bonferroni        
  6 insecti omni      1     Student's t-test Bonferroni        
    label                                        
    <chr>                                        
  1 list(~italic(p)[Bonferroni-corrected]==1.000)
  2 list(~italic(p)[Bonferroni-corrected]==1.000)
  3 list(~italic(p)[Bonferroni-corrected]==1.000)
  4 list(~italic(p)[Bonferroni-corrected]==1.000)
  5 list(~italic(p)[Bonferroni-corrected]==0.979)
  6 list(~italic(p)[Bonferroni-corrected]==1.000)

  [[2]]
  # A tibble: 6 x 11
    group1  group2  statistic p.value alternative method            distribution
    <chr>   <chr>       <dbl>   <dbl> <chr>       <chr>             <chr>       
  1 carni   herbi        2.17       1 two.sided   Games-Howell test q           
  2 carni   insecti     -2.17       1 two.sided   Games-Howell test q           
  3 carni   omni         1.10       1 two.sided   Games-Howell test q           
  4 herbi   insecti     -2.41       1 two.sided   Games-Howell test q           
  5 herbi   omni        -1.87       1 two.sided   Games-Howell test q           
  6 insecti omni         2.19       1 two.sided   Games-Howell test q           
    p.adjustment test.details      p.value.adjustment
    <chr>        <chr>             <chr>             
  1 none         Games-Howell test Bonferroni        
  2 none         Games-Howell test Bonferroni        
  3 none         Games-Howell test Bonferroni        
  4 none         Games-Howell test Bonferroni        
  5 none         Games-Howell test Bonferroni        
  6 none         Games-Howell test Bonferroni        
    label                                        
    <chr>                                        
  1 list(~italic(p)[Bonferroni-corrected]==1.000)
  2 list(~italic(p)[Bonferroni-corrected]==1.000)
  3 list(~italic(p)[Bonferroni-corrected]==1.000)
  4 list(~italic(p)[Bonferroni-corrected]==1.000)
  5 list(~italic(p)[Bonferroni-corrected]==1.000)
  6 list(~italic(p)[Bonferroni-corrected]==1.000)

  [[3]]
  # A tibble: 6 x 11
    group1  group2  statistic p.value alternative method               
    <chr>   <chr>       <dbl>   <dbl> <chr>       <chr>                
  1 carni   herbi       0.582  0.561  two.sided   Dunn's all-pairs test
  2 carni   insecti     1.88   0.0595 two.sided   Dunn's all-pairs test
  3 carni   omni        1.14   0.254  two.sided   Dunn's all-pairs test
  4 herbi   insecti     1.63   0.102  two.sided   Dunn's all-pairs test
  5 herbi   omni        0.717  0.474  two.sided   Dunn's all-pairs test
  6 insecti omni        1.14   0.254  two.sided   Dunn's all-pairs test
    distribution p.adjustment test.details p.value.adjustment
    <chr>        <chr>        <chr>        <chr>             
  1 z            none         Dunn test    None              
  2 z            none         Dunn test    None              
  3 z            none         Dunn test    None              
  4 z            none         Dunn test    None              
  5 z            none         Dunn test    None              
  6 z            none         Dunn test    None              
    label                               
    <chr>                               
  1 list(~italic(p)[uncorrected]==0.561)
  2 list(~italic(p)[uncorrected]==0.060)
  3 list(~italic(p)[uncorrected]==0.254)
  4 list(~italic(p)[uncorrected]==0.102)
  5 list(~italic(p)[uncorrected]==0.474)
  6 list(~italic(p)[uncorrected]==0.254)

  [[4]]
  # A tibble: 6 x 10
    group1  group2  estimate conf.level conf.low conf.high p.value
    <chr>   <chr>      <dbl>      <dbl>    <dbl>     <dbl>   <dbl>
  1 carni   herbi   -0.0323        0.95  -0.248     0.184    0.898
  2 carni   insecti  0.0451        0.95  -0.0484    0.139    0.898
  3 carni   omni     0.00520       0.95  -0.114     0.124    0.898
  4 herbi   insecti  0.0774        0.95  -0.133     0.288    0.898
  5 herbi   omni     0.0375        0.95  -0.182     0.257    0.898
  6 insecti omni    -0.0399        0.95  -0.142     0.0625   0.898
    test.details              p.value.adjustment
    <chr>                     <chr>             
  1 Yuen's trimmed means test FDR               
  2 Yuen's trimmed means test FDR               
  3 Yuen's trimmed means test FDR               
  4 Yuen's trimmed means test FDR               
  5 Yuen's trimmed means test FDR               
  6 Yuen's trimmed means test FDR               
    label                                 
    <chr>                                 
  1 list(~italic(p)[FDR-corrected]==0.898)
  2 list(~italic(p)[FDR-corrected]==0.898)
  3 list(~italic(p)[FDR-corrected]==0.898)
  4 list(~italic(p)[FDR-corrected]==0.898)
  5 list(~italic(p)[FDR-corrected]==0.898)
  6 list(~italic(p)[FDR-corrected]==0.898)

  [[5]]
  # A tibble: 3 x 6
    group1 group2 p.value test.details     p.value.adjustment
    <chr>  <chr>    <dbl> <chr>            <chr>             
  1 PG     PG-13  0.316   Student's t-test Holm              
  2 PG     R      0.00283 Student's t-test Holm              
  3 PG-13  R      0.00310 Student's t-test Holm              
    label                                  
    <chr>                                  
  1 list(~italic(p)[Holm-corrected]==0.316)
  2 list(~italic(p)[Holm-corrected]==0.003)
  3 list(~italic(p)[Holm-corrected]==0.003)